1. Field of the Invention
This invention relates to an adaptive method of suppressing video signal echoes in advanced television (TV) sets.
More particularly, the invention relates to an adaptive method of suppressing video signal echoes x(k) in equalizers of TV sets incorporating digital filters whose coefficients are updated in an adaptive and iterative manner using a modified LMS (Least Mean Square) algorithm until the difference, or output error, between a desired output signal d(k)--called the reference signal--and an outgoing signal y(k) from the equalizer is progressively reduced.
2. Discussion of the Related Art
As is well known, one of the main causes for deterioration of the TV picture quality on the displays of conventional analog TV receivers is the appearance of duplicates of the original signal which, by overlapping the primary image, create a so-called echo effect. TV echoes, best known as ghosts, are usually due to reflections undergone by the primary TV signal against fixed rebound surfaces, such as mountains, buildings, etc. Also, some moving objects, such as airplanes flying over the area between the TV transmitter and the TV receiver, may scatter the TV signal and generate such noise.
The echoes thus generated are presented to the receiver in the form of delayed, attenuated, and distorted duplicates of the primary image. The video echo phenomenon actually is a major deteriorating factor of TV pictures, and its influence is most noticeable on the recently introduced Improved Quality (IQTV) receivers and the High Definition (HDTV) wide screen TVs.
In fact, with such new generation TV sets, the doubled line scan frequency and the use of large size screens tend to enhance the ghost effect on the display.
Accordingly, while a standard TV receiver equipped with a channel equalizer can already provide a picture of significantly improved quality, the use of an equalizer is bound to become an absolute necessity in IQTV and HDTV sets.
For a better understanding of the invention aspects, a review of the traditional ghost-suppressing techniques is provided below. The ghost cancelling operation includes one of filtering the TV signal being input to the TV receiver.
The TV ghost cancelling process is usually based on a so-called equalization system. Since the characteristics of an RF video channel vary over time because they are strictly dependent on environment and operating conditions, the ghost cancelling process is most effective when an automatic equalization system of the adaptive type is used. Two different approaches are available for implementing a video channel equalization system; namely a direct method and an indirect method.
Many video channel equalizers are based on the identification of the video channel pulsive response and its inverse. This identification enables the coefficients of the system filters to be generated by a direct method. Although this technique performs accurately, it is highly complex from a computational standpoint.
Alternatively, an adaptive system based on an indirect method utilizes a ghost-suppressing algorithm which updates the values of the internal filter coefficients in an adaptive manner; that is, so as to gradually reduce the difference between a desired reference signal and the outgoing signal from the ghost-suppressing system.
The filtering section of an equalizer system may be either implemented by an F.I.R. method (Finite Impulse Response) or an I.I.R. (Infinite Impulse Response) method. The former uses FIR filters to approximate the infinite pulsive response from the video channel equalizer. The latter suppresses the ghost effect by means of IIR filters; that is, feedback FIR filters.
The first-mentioned method may be defined as a classic type and is widely used for suppressing TV echoes. However, while ensuring stability for its filters, it can provide no equalizer of the adaptive type unless recourse to a complicated hardware structure is made. In fact, each pole of the pulsive response from the ghost suppressor can only be approximated by a large number of zeroes, which significantly increases the number of the internal multipliers of the system.
The second-mentioned method provides for the use of an equalizer based on IIR filters. This method streamlines the system hardware and reduces the volume of the calculations required for the ghost suppressing operation, but its stability is not always ensured. Many adaptive algorithms intended for application to IIR filters have been defined and tested for stability, rate of convergence, and capability to remove TV echoes. Such algorithms stem from a classic algorithm known as Least Mean Square (LMS).
The updating rules for the coefficients of an LMS algorithm are the following: EQU ai(k)=ai(k-1)+m y(k-1-i)e(k-1)1&lt;i&lt;N bj(k)=bj (k-1)+s (k-1-j)e(k-1)1&lt;j&lt;M (1)
where, e(k)=d(k)-y(k).
The Greek letters m and s designate the adaptation pitch (also known as convergence factor) for the FIR and IIR filters, respectively; ai and bj are the coefficients of the IIR and FIR filters, respectively; x(k) and y(k) designate samples of the input and output signals to/from the equalizer; and e(k) is the output error, i.e. the difference between the target signal d(k) and the system output y(k).
Simulations carried out by applying the classic LMS algorithm to an IIR filter with M coefficients or taps have proved unsatisfactory especially in the presence of large amplitude echoes. Nonetheless, an analysis of the aggregate of the associated coefficients shows that:
--most of the coefficients have near-null values; PA1 --the non-null values form clusters or local peaks during the adaptation process; and PA1 --the locations of such clusters on the delay line of the filter are closely related to the delays of the echoes which overlap the primary image. PA1 --the LMS algorithm be applied to each combing filter for a fixed number (e.g. four) of iterations; PA1 --the subfilter coefficients be gathered into the original aggregate of M coefficients; and PA1 --the LMS algorithm be applied to the most significant of the coefficients thus determined.
FIG. 2 shows a typical pattern for the coefficients generated by the application of an LMS algorithm until a lower output error than a predetermined threshold is obtained. The simulation conditions are entered into Table 1 in FIG. 3, where the echo delays are set forth as sample numbers with the sampling period of 74 ns.
A more up-to-date ghost cancelling technique provides a determination of the locations of the largest amplitude clusters corresponding to the main signal overlapping echoes such that only the most significant filter coefficients will be updated, thereby reducing the computation volume and improving the algorithm rate of convergence. Thus, the secondary coefficients can be cleared as the corresponding multipliers are removed, which will relieve the structural complexity of the equalizer.
Understandably, the reduced coefficient aggregate is bound to differ, at the end of the adaptation process, from the configuration of the original aggregate. However, by suitably selecting the coefficients of the new reduced configurations, it becomes possible to re-create the right pulsive response from the echo suppressing system, even with a smaller number of multipliers. To accomplish this recreation, a so-called "combing" method has been used whereby several reduced coefficient configurations are selected by a "combing" procedure, starting with the original configuration. In other words, to create N comb filters, with M initial filter coefficients, one coefficient is selected every K periods, where K=M/N integer, starting with the first coefficient of the original filter.
The next "comb" of coefficients is obtained by shifting the immediately preceding comb through one coefficient, as follows:
______________________________________ Initial Configuration: a1 a2 a3 a4 a5 a6 a7 a8 a9 ______________________________________ First Comb: a1 a4 a7 Second Comb: a2 a5 a8 Third Comb: a3 a6 a9 ______________________________________
In this way, the combination of the partial coefficient configurations will cover the original configuration, leaving their intersections empty. Accordingly, it can be ensured that the merging of the partial configurations which results from the adaptation process will approximate, in an optimum manner, the original configuration of the coefficients, following the application of the algorithm.
Once the K combing filters with N coefficients each are selected, one of the early methods under consideration implied that:
This sequence of operations defines an algorithm called CDFF (Cluster Detection with Fixed-convergence Factor) which has led to considerable improvements over the mere application of the LMS algorithm.
Further improvements in the performance of that algorithm have been attained by changing the adaptation pitch m (Equation 1) according to the value of the output error. In fact, the parameter m directly governs both stability and rate of convergence. However, this parameter also controls the amplitude of the coefficient fluctuations about their final value (Granularity Error); the higher the value of m, the greater is the algorithm rate of convergence, but the greater becomes the granularity error also. In other words, the value of m and the rate of convergence are directly proportional.
To obviate this drawback, it was introduced to vary the adaptation step with respect to the error e(k) according to a linear law whereby the angular coefficient changes between two values upon e(k) going below a given threshold. In this way, so far as the error e(k) between the cleaned signal from the adaptive equalizer and the desired signal stay high, m will maintain high values which decrease quickly with a decreasing error e(k). Upon e(k) dropping below a predetermined threshold, m will attain low values which decrease very slowly as e(k) decreases.
The above-described situation is depicted schematically in FIG. 3. Despite the improvement, the CDVF (Cluster Detection with Variable-convergence Factor) algorithm described above exhibits yet another drawback. During simulations, a delay line was considered which could accommodate post-echoes carrying delays of up to 14.6 .mu.s. Accordingly, on completion of the combing step which presupposes the creation of K=3 comb filters, a further subfilter is obtained which consists of the M/3=N highest modulo taps. In the presence of three echoes, the N coefficients are substantially equally distributed over three clusters (approximately N/3 taps per cluster). In the presence of a single echo, all of the N coefficient will instead correspond to the single cluster provided.
Since the algorithm time of convergence is heavily dependent not only on the cluster "framing" --i.e. the determination of the peak largest coefficient--but also on the determination of the actual width of the cluster, progressive deterioration of the algorithm performance was encountered as the number of the echoes decreased, due to the above-mentioned over abundance of coefficients.
It is, therefore, an object of this invention to provide a novel adaptive method for suppressing video signal echoes in equalizer devices which substantially avoids the drawbacks associated with the various prior art methods mentioned above.